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Supply Chain Logistics

Through IoT integration and real-time data processing, CrateDB enables efficient tracking of shipments and inventory management. Its scalability and analytics capabilities facilitate route optimization and demand forecasting, enhancing operational efficiency and customer satisfaction in logistics operations.

Real-time Shipment Tracking

Live Location Updates: CrateDB enables logistics companies to capture and analyze real-time location data from GPS-enabled devices attached to shipments, providing accurate and up-to-date information on the current whereabouts of goods in transit.

Event Monitoring and Alerts: CrateDB can monitor events such as package scans, departures, arrivals, and delays in real-time. It triggers alerts and notifications based on predefined criteria, allowing logistics companies to proactively address issues and keep customers informed about the status of their shipments.


Warehouse Operations Optimization

Workflow Analysis: CrateDB enables logistics companies to analyze data on warehouse operations, including order picking, packing, and shipping processes. By identifying inefficiencies and bottlenecks in workflows, companies can streamline operations, reduce labor costs, and improve overall productivity.

Resource Allocation Optimization: CrateDB helps optimize resource allocation within warehouses by analyzing data on inventory levels, order volumes, and workforce availability. By dynamically allocating resources based on real-time demand, companies can minimize stockouts, improve order fulfillment rates, and enhance customer satisfaction.


Predictive Maintenance

Condition Monitoring: CrateDB facilitates continuous monitoring of equipment health by collecting and analyzing real-time data from sensors and IoT devices. By monitoring parameters such as temperature, vibration, and performance metrics, logistics companies can detect early signs of equipment degradation or malfunction.

Predictive Analytics and Failure Prediction: CrateDB can be used for predictive analytics techniques, such as machine learning algorithms, to analyze historical and real-time data and predict potential equipment failures. By identifying patterns and anomalies indicative of impending failures, logistics companies can schedule maintenance proactively, minimize downtime, and prevent costly equipment breakdowns.

Logistics networks embody complexity and interconnectivity, all fine-tuned for peak efficiency. In the world of logistics, efficiency reigns supreme - it's about cutting down miles, reducing time, saving costs, and ultimately, ensuring customer satisfaction. Consider the roles of asset tagging, QR codes, real-time tracking, swift deliveries, temperature monitoring, in-vehicle telematics, and stringent quality checks. In today's logistics landscape, data is foundational, not supplementary. It forms the core of contemporary supply chains, enabling transporters to implement a just-in-time delivery system with the latest insights on inventory, status, location, and demand. By reading these two case studies below, discover how logistic companies leverage CrateDB to power real-time analytics in high-volume data architectures.


Case Study: TGW Logistics

TGW Logistics Group is one of the leading international suppliers of material handling solutions. For more than 50 years, the Austrian specialist has implemented automated systems for its international customers, including brands from A as in Adidas to Z as in Zalando. As a systems integrator, TGW plans, produces, and implements complex logistics centers, from mechatronic products and robots to control systems and software. TGW Logistics Group has subsidiaries in Europe, China and the US and more than 4,000 employees worldwide.

Key objectives

  • Get real-time insights into warehouse activities.
  • Visualize, aggregate, and analyze data from the different warehouses worldwide.

Main technical challenges

  • Real-time analysis of data coming from multiple sensors and systems.
  • Consolidate local data silos centrally – with edge and cloud support.
  • Query significant volumes of data with many different types.
  • Enabling a Digital Twin application where users can correlate different data series to perform detailed error analysis and go back in time.




sensors per warehouse center

Why CrateDB?

  • One single database to collect sensor data and business data at scale.
  • Simple integration into existing open-source and business application stack with standard SQL.
  • Simple deployment in any hybrid scenario.

Data Innovation Summit 2024

Digital Twins and Generative AI: How TGW Revolutionizes Warehouse Operations with CrateDB's Combination of Time Series, Documents, and Vectors

In this talk, TGW Logistics showcases their use of CrateDB to optimize distribution centers. With up to half a million items handled daily, they focus on automation and data-driven decisions.

"CrateDB allows us to operate on any Cloud and on-prem/Edge with simplicity and stellar performance, and significant cost advantages."
Harald Schröpf CEO TGW Logistics

The Results

  • Increased database performance for high data volumes, handling 100,000+ messages every few seconds
  • Improved predictive modeling accuracy, enabling AI teams to ingest and query full datasets from warehouses worldwide
  • Provide customers with more visibility and insights to optimize warehouse operations
  • Scale quickly and easily to handle TGW’s ongoing growth, automatically rebalancing data as new nodes are added to the cluster
  • Strengthen TGW’s competitive advantage by using data-driven insights to provide more cost-effective customer offers